renaissance-movie-lens_0
[2024-09-04T20:49:39.789Z] Running test renaissance-movie-lens_0 ...
[2024-09-04T20:49:39.789Z] ===============================================
[2024-09-04T20:49:39.789Z] renaissance-movie-lens_0 Start Time: Wed Sep 4 20:49:38 2024 Epoch Time (ms): 1725482978843
[2024-09-04T20:49:39.789Z] variation: NoOptions
[2024-09-04T20:49:39.789Z] JVM_OPTIONS:
[2024-09-04T20:49:39.789Z] { \
[2024-09-04T20:49:39.789Z] echo ""; echo "TEST SETUP:"; \
[2024-09-04T20:49:39.789Z] echo "Nothing to be done for setup."; \
[2024-09-04T20:49:39.789Z] mkdir -p "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17254824266479/renaissance-movie-lens_0"; \
[2024-09-04T20:49:39.789Z] cd "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17254824266479/renaissance-movie-lens_0"; \
[2024-09-04T20:49:39.789Z] echo ""; echo "TESTING:"; \
[2024-09-04T20:49:39.789Z] "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/jdkbinary/j2sdk-image/bin/java" --add-opens java.base/java.lang=ALL-UNNAMED --add-opens java.base/java.util=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/sun.nio.ch=ALL-UNNAMED --add-opens java.base/java.lang.invoke=ALL-UNNAMED -jar "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../../jvmtest/perf/renaissance/renaissance.jar" --json ""/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17254824266479/renaissance-movie-lens_0"/movie-lens.json" movie-lens; \
[2024-09-04T20:49:39.789Z] if [ $? -eq 0 ]; then echo "-----------------------------------"; echo "renaissance-movie-lens_0""_PASSED"; echo "-----------------------------------"; cd /home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/..; rm -f -r "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17254824266479/renaissance-movie-lens_0"; else echo "-----------------------------------"; echo "renaissance-movie-lens_0""_FAILED"; echo "-----------------------------------"; fi; \
[2024-09-04T20:49:39.789Z] echo ""; echo "TEST TEARDOWN:"; \
[2024-09-04T20:49:39.789Z] echo "Nothing to be done for teardown."; \
[2024-09-04T20:49:39.789Z] } 2>&1 | tee -a "/home/jenkins/workspace/Test_openjdk17_hs_extended.perf_aarch64_linux/aqa-tests/TKG/../TKG/output_17254824266479/TestTargetResult";
[2024-09-04T20:49:39.789Z]
[2024-09-04T20:49:39.789Z] TEST SETUP:
[2024-09-04T20:49:39.789Z] Nothing to be done for setup.
[2024-09-04T20:49:39.789Z]
[2024-09-04T20:49:39.789Z] TESTING:
[2024-09-04T20:49:41.715Z] Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
[2024-09-04T20:49:43.641Z] NOTE: 'movie-lens' benchmark uses Spark local executor with 8 (out of 8) threads.
[2024-09-04T20:49:45.567Z] Got 100004 ratings from 671 users on 9066 movies.
[2024-09-04T20:49:45.567Z] Training: 60056, validation: 20285, test: 19854
[2024-09-04T20:49:45.567Z] ====== movie-lens (apache-spark) [default], iteration 0 started ======
[2024-09-04T20:49:45.567Z] GC before operation: completed in 32.715 ms, heap usage 153.375 MB -> 39.368 MB.
[2024-09-04T20:49:49.668Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T20:49:52.641Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T20:49:54.568Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T20:49:56.518Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T20:49:57.456Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T20:49:58.393Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T20:49:59.331Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T20:50:01.253Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T20:50:01.253Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-04T20:50:01.253Z] The best model improves the baseline by 14.43%.
[2024-09-04T20:50:01.253Z] Movies recommended for you:
[2024-09-04T20:50:01.253Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T20:50:01.253Z] There is no way to check that no silent failure occurred.
[2024-09-04T20:50:01.253Z] ====== movie-lens (apache-spark) [default], iteration 0 completed (15444.601 ms) ======
[2024-09-04T20:50:01.253Z] ====== movie-lens (apache-spark) [default], iteration 1 started ======
[2024-09-04T20:50:01.253Z] GC before operation: completed in 67.807 ms, heap usage 1.300 GB -> 57.396 MB.
[2024-09-04T20:50:03.176Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T20:50:05.099Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T20:50:07.023Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T20:50:07.961Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T20:50:08.899Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T20:50:09.835Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T20:50:10.772Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T20:50:11.708Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T20:50:12.665Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-04T20:50:12.666Z] The best model improves the baseline by 14.43%.
[2024-09-04T20:50:12.666Z] Movies recommended for you:
[2024-09-04T20:50:12.666Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T20:50:12.666Z] There is no way to check that no silent failure occurred.
[2024-09-04T20:50:12.666Z] ====== movie-lens (apache-spark) [default], iteration 1 completed (11201.574 ms) ======
[2024-09-04T20:50:12.666Z] ====== movie-lens (apache-spark) [default], iteration 2 started ======
[2024-09-04T20:50:12.666Z] GC before operation: completed in 61.488 ms, heap usage 476.825 MB -> 53.429 MB.
[2024-09-04T20:50:14.595Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T20:50:15.533Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T20:50:17.459Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T20:50:18.409Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T20:50:20.066Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T20:50:21.050Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T20:50:21.999Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T20:50:22.971Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T20:50:22.971Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-04T20:50:22.971Z] The best model improves the baseline by 14.43%.
[2024-09-04T20:50:22.971Z] Movies recommended for you:
[2024-09-04T20:50:22.971Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T20:50:22.971Z] There is no way to check that no silent failure occurred.
[2024-09-04T20:50:22.971Z] ====== movie-lens (apache-spark) [default], iteration 2 completed (10289.679 ms) ======
[2024-09-04T20:50:22.971Z] ====== movie-lens (apache-spark) [default], iteration 3 started ======
[2024-09-04T20:50:22.971Z] GC before operation: completed in 59.342 ms, heap usage 350.271 MB -> 57.102 MB.
[2024-09-04T20:50:23.908Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T20:50:25.835Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T20:50:26.773Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T20:50:28.872Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T20:50:29.810Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T20:50:30.746Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T20:50:30.746Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T20:50:31.686Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T20:50:32.624Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-04T20:50:32.624Z] The best model improves the baseline by 14.43%.
[2024-09-04T20:50:32.624Z] Movies recommended for you:
[2024-09-04T20:50:32.624Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T20:50:32.624Z] There is no way to check that no silent failure occurred.
[2024-09-04T20:50:32.624Z] ====== movie-lens (apache-spark) [default], iteration 3 completed (9493.632 ms) ======
[2024-09-04T20:50:32.624Z] ====== movie-lens (apache-spark) [default], iteration 4 started ======
[2024-09-04T20:50:32.624Z] GC before operation: completed in 62.957 ms, heap usage 1.356 GB -> 58.789 MB.
[2024-09-04T20:50:33.562Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T20:50:35.485Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T20:50:36.420Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T20:50:38.353Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T20:50:38.353Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T20:50:39.291Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T20:50:40.232Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T20:50:41.170Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T20:50:41.170Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-04T20:50:41.170Z] The best model improves the baseline by 14.43%.
[2024-09-04T20:50:41.170Z] Movies recommended for you:
[2024-09-04T20:50:41.170Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T20:50:41.170Z] There is no way to check that no silent failure occurred.
[2024-09-04T20:50:41.170Z] ====== movie-lens (apache-spark) [default], iteration 4 completed (9181.586 ms) ======
[2024-09-04T20:50:41.170Z] ====== movie-lens (apache-spark) [default], iteration 5 started ======
[2024-09-04T20:50:41.170Z] GC before operation: completed in 65.348 ms, heap usage 1.303 GB -> 58.896 MB.
[2024-09-04T20:50:43.096Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T20:50:44.034Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T20:50:45.963Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T20:50:46.906Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T20:50:47.845Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T20:50:48.783Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T20:50:49.720Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T20:50:50.657Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T20:50:50.657Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-04T20:50:50.657Z] The best model improves the baseline by 14.43%.
[2024-09-04T20:50:50.657Z] Movies recommended for you:
[2024-09-04T20:50:50.657Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T20:50:50.657Z] There is no way to check that no silent failure occurred.
[2024-09-04T20:50:50.657Z] ====== movie-lens (apache-spark) [default], iteration 5 completed (9304.676 ms) ======
[2024-09-04T20:50:50.657Z] ====== movie-lens (apache-spark) [default], iteration 6 started ======
[2024-09-04T20:50:50.657Z] GC before operation: completed in 64.329 ms, heap usage 191.652 MB -> 54.151 MB.
[2024-09-04T20:50:52.582Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T20:50:53.519Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T20:50:55.446Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T20:50:56.385Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T20:50:57.321Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T20:50:58.260Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T20:50:59.202Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T20:51:00.142Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T20:51:00.142Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-04T20:51:00.142Z] The best model improves the baseline by 14.43%.
[2024-09-04T20:51:00.142Z] Movies recommended for you:
[2024-09-04T20:51:00.142Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T20:51:00.142Z] There is no way to check that no silent failure occurred.
[2024-09-04T20:51:00.142Z] ====== movie-lens (apache-spark) [default], iteration 6 completed (9292.274 ms) ======
[2024-09-04T20:51:00.142Z] ====== movie-lens (apache-spark) [default], iteration 7 started ======
[2024-09-04T20:51:00.142Z] GC before operation: completed in 65.046 ms, heap usage 305.049 MB -> 54.476 MB.
[2024-09-04T20:51:02.068Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T20:51:03.006Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T20:51:04.930Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T20:51:05.869Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T20:51:06.806Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T20:51:07.744Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T20:51:08.683Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T20:51:09.621Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T20:51:09.621Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-04T20:51:09.621Z] The best model improves the baseline by 14.43%.
[2024-09-04T20:51:09.621Z] Movies recommended for you:
[2024-09-04T20:51:09.621Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T20:51:09.621Z] There is no way to check that no silent failure occurred.
[2024-09-04T20:51:09.621Z] ====== movie-lens (apache-spark) [default], iteration 7 completed (9254.336 ms) ======
[2024-09-04T20:51:09.621Z] ====== movie-lens (apache-spark) [default], iteration 8 started ======
[2024-09-04T20:51:09.621Z] GC before operation: completed in 65.613 ms, heap usage 348.257 MB -> 54.663 MB.
[2024-09-04T20:51:11.546Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T20:51:12.484Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T20:51:14.409Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T20:51:15.375Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T20:51:16.315Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T20:51:17.253Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T20:51:18.192Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T20:51:18.192Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T20:51:19.129Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-04T20:51:19.129Z] The best model improves the baseline by 14.43%.
[2024-09-04T20:51:19.129Z] Movies recommended for you:
[2024-09-04T20:51:19.129Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T20:51:19.129Z] There is no way to check that no silent failure occurred.
[2024-09-04T20:51:19.129Z] ====== movie-lens (apache-spark) [default], iteration 8 completed (9213.101 ms) ======
[2024-09-04T20:51:19.129Z] ====== movie-lens (apache-spark) [default], iteration 9 started ======
[2024-09-04T20:51:19.129Z] GC before operation: completed in 64.848 ms, heap usage 391.145 MB -> 54.591 MB.
[2024-09-04T20:51:20.068Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T20:51:22.009Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T20:51:22.951Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T20:51:23.890Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T20:51:24.828Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T20:51:25.766Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T20:51:26.704Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T20:51:27.643Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T20:51:27.643Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-04T20:51:27.643Z] The best model improves the baseline by 14.43%.
[2024-09-04T20:51:27.643Z] Movies recommended for you:
[2024-09-04T20:51:27.643Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T20:51:27.643Z] There is no way to check that no silent failure occurred.
[2024-09-04T20:51:27.643Z] ====== movie-lens (apache-spark) [default], iteration 9 completed (8879.386 ms) ======
[2024-09-04T20:51:27.643Z] ====== movie-lens (apache-spark) [default], iteration 10 started ======
[2024-09-04T20:51:27.643Z] GC before operation: completed in 66.457 ms, heap usage 1.372 GB -> 59.259 MB.
[2024-09-04T20:51:29.576Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T20:51:31.499Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T20:51:32.438Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T20:51:34.364Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T20:51:35.302Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T20:51:36.241Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T20:51:36.241Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T20:51:37.180Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T20:51:38.120Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-04T20:51:38.120Z] The best model improves the baseline by 14.43%.
[2024-09-04T20:51:38.120Z] Movies recommended for you:
[2024-09-04T20:51:38.120Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T20:51:38.120Z] There is no way to check that no silent failure occurred.
[2024-09-04T20:51:38.120Z] ====== movie-lens (apache-spark) [default], iteration 10 completed (9879.399 ms) ======
[2024-09-04T20:51:38.120Z] ====== movie-lens (apache-spark) [default], iteration 11 started ======
[2024-09-04T20:51:38.120Z] GC before operation: completed in 70.260 ms, heap usage 900.776 MB -> 60.092 MB.
[2024-09-04T20:51:39.058Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T20:51:40.986Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T20:51:42.911Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T20:51:43.849Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T20:51:44.794Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T20:51:45.734Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T20:51:46.673Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T20:51:47.612Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T20:51:47.612Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-04T20:51:47.612Z] The best model improves the baseline by 14.43%.
[2024-09-04T20:51:48.549Z] Movies recommended for you:
[2024-09-04T20:51:48.549Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T20:51:48.549Z] There is no way to check that no silent failure occurred.
[2024-09-04T20:51:48.549Z] ====== movie-lens (apache-spark) [default], iteration 11 completed (10285.914 ms) ======
[2024-09-04T20:51:48.549Z] ====== movie-lens (apache-spark) [default], iteration 12 started ======
[2024-09-04T20:51:48.549Z] GC before operation: completed in 65.575 ms, heap usage 85.750 MB -> 57.159 MB.
[2024-09-04T20:51:49.491Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T20:51:51.417Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T20:51:53.345Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T20:51:54.283Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T20:51:55.244Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T20:51:56.182Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T20:51:57.120Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T20:51:58.057Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T20:51:58.995Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-04T20:51:58.995Z] The best model improves the baseline by 14.43%.
[2024-09-04T20:51:58.995Z] Movies recommended for you:
[2024-09-04T20:51:58.995Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T20:51:58.995Z] There is no way to check that no silent failure occurred.
[2024-09-04T20:51:58.995Z] ====== movie-lens (apache-spark) [default], iteration 12 completed (10481.464 ms) ======
[2024-09-04T20:51:58.995Z] ====== movie-lens (apache-spark) [default], iteration 13 started ======
[2024-09-04T20:51:58.995Z] GC before operation: completed in 70.450 ms, heap usage 484.817 MB -> 54.848 MB.
[2024-09-04T20:52:00.929Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T20:52:01.883Z] RMSE (validation) = 2.1340923217118064 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T20:52:03.810Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T20:52:05.741Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T20:52:06.680Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T20:52:07.617Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T20:52:08.561Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T20:52:09.500Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T20:52:09.500Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-04T20:52:09.500Z] The best model improves the baseline by 14.43%.
[2024-09-04T20:52:09.500Z] Movies recommended for you:
[2024-09-04T20:52:09.500Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T20:52:09.500Z] There is no way to check that no silent failure occurred.
[2024-09-04T20:52:09.500Z] ====== movie-lens (apache-spark) [default], iteration 13 completed (10533.756 ms) ======
[2024-09-04T20:52:09.500Z] ====== movie-lens (apache-spark) [default], iteration 14 started ======
[2024-09-04T20:52:09.500Z] GC before operation: completed in 72.754 ms, heap usage 1.765 GB -> 60.509 MB.
[2024-09-04T20:52:10.467Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T20:52:12.396Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T20:52:13.336Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T20:52:15.262Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T20:52:16.199Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T20:52:17.137Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T20:52:17.137Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T20:52:18.074Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T20:52:18.074Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-04T20:52:18.074Z] The best model improves the baseline by 14.43%.
[2024-09-04T20:52:18.074Z] Movies recommended for you:
[2024-09-04T20:52:18.074Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T20:52:18.074Z] There is no way to check that no silent failure occurred.
[2024-09-04T20:52:18.074Z] ====== movie-lens (apache-spark) [default], iteration 14 completed (9166.084 ms) ======
[2024-09-04T20:52:18.074Z] ====== movie-lens (apache-spark) [default], iteration 15 started ======
[2024-09-04T20:52:19.010Z] GC before operation: completed in 74.032 ms, heap usage 1.281 GB -> 61.554 MB.
[2024-09-04T20:52:19.946Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T20:52:21.884Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T20:52:22.823Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T20:52:24.764Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T20:52:25.700Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T20:52:25.700Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T20:52:26.637Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T20:52:27.574Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T20:52:27.574Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-04T20:52:27.574Z] The best model improves the baseline by 14.43%.
[2024-09-04T20:52:27.574Z] Movies recommended for you:
[2024-09-04T20:52:27.574Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T20:52:27.574Z] There is no way to check that no silent failure occurred.
[2024-09-04T20:52:27.574Z] ====== movie-lens (apache-spark) [default], iteration 15 completed (9414.669 ms) ======
[2024-09-04T20:52:27.574Z] ====== movie-lens (apache-spark) [default], iteration 16 started ======
[2024-09-04T20:52:28.517Z] GC before operation: completed in 67.552 ms, heap usage 301.433 MB -> 59.365 MB.
[2024-09-04T20:52:29.459Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T20:52:31.386Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T20:52:32.325Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T20:52:34.250Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T20:52:35.188Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T20:52:36.125Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T20:52:37.063Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T20:52:37.999Z] RMSE (validation) = 0.9001440981626694 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T20:52:37.999Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-04T20:52:37.999Z] The best model improves the baseline by 14.43%.
[2024-09-04T20:52:37.999Z] Movies recommended for you:
[2024-09-04T20:52:37.999Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T20:52:37.999Z] There is no way to check that no silent failure occurred.
[2024-09-04T20:52:37.999Z] ====== movie-lens (apache-spark) [default], iteration 16 completed (9676.641 ms) ======
[2024-09-04T20:52:37.999Z] ====== movie-lens (apache-spark) [default], iteration 17 started ======
[2024-09-04T20:52:37.999Z] GC before operation: completed in 65.951 ms, heap usage 498.099 MB -> 57.949 MB.
[2024-09-04T20:52:39.925Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T20:52:40.862Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T20:52:42.840Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T20:52:44.771Z] RMSE (validation) = 1.004540774800519 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T20:52:44.771Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T20:52:45.708Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T20:52:47.636Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T20:52:47.636Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T20:52:48.574Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-04T20:52:48.574Z] The best model improves the baseline by 14.43%.
[2024-09-04T20:52:48.574Z] Movies recommended for you:
[2024-09-04T20:52:48.574Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T20:52:48.574Z] There is no way to check that no silent failure occurred.
[2024-09-04T20:52:48.574Z] ====== movie-lens (apache-spark) [default], iteration 17 completed (10407.561 ms) ======
[2024-09-04T20:52:48.574Z] ====== movie-lens (apache-spark) [default], iteration 18 started ======
[2024-09-04T20:52:48.574Z] GC before operation: completed in 68.896 ms, heap usage 1.741 GB -> 61.742 MB.
[2024-09-04T20:52:50.501Z] RMSE (validation) = 3.6219689545487506 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T20:52:51.439Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T20:52:53.373Z] RMSE (validation) = 1.3105189674610933 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T20:52:54.311Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T20:52:55.250Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T20:52:56.188Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T20:52:57.125Z] RMSE (validation) = 0.9275717391338142 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T20:52:58.064Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T20:52:58.064Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-04T20:52:58.064Z] The best model improves the baseline by 14.43%.
[2024-09-04T20:52:58.064Z] Movies recommended for you:
[2024-09-04T20:52:58.064Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T20:52:58.064Z] There is no way to check that no silent failure occurred.
[2024-09-04T20:52:58.064Z] ====== movie-lens (apache-spark) [default], iteration 18 completed (10191.589 ms) ======
[2024-09-04T20:52:58.064Z] ====== movie-lens (apache-spark) [default], iteration 19 started ======
[2024-09-04T20:52:59.001Z] GC before operation: completed in 65.176 ms, heap usage 117.183 MB -> 54.620 MB.
[2024-09-04T20:52:59.939Z] RMSE (validation) = 3.621968954548751 for the model trained with rank = 8, lambda = 5.0, and numIter = 20.
[2024-09-04T20:53:01.864Z] RMSE (validation) = 2.134092321711807 for the model trained with rank = 10, lambda = 2.0, and numIter = 20.
[2024-09-04T20:53:03.787Z] RMSE (validation) = 1.3105189674610935 for the model trained with rank = 12, lambda = 1.0, and numIter = 20.
[2024-09-04T20:53:04.724Z] RMSE (validation) = 1.0045407748005193 for the model trained with rank = 8, lambda = 0.05, and numIter = 20.
[2024-09-04T20:53:05.661Z] RMSE (validation) = 1.2218330581874075 for the model trained with rank = 10, lambda = 0.01, and numIter = 10.
[2024-09-04T20:53:06.598Z] RMSE (validation) = 1.1174953766371012 for the model trained with rank = 8, lambda = 0.02, and numIter = 10.
[2024-09-04T20:53:07.535Z] RMSE (validation) = 0.9275717391338141 for the model trained with rank = 12, lambda = 0.1, and numIter = 10.
[2024-09-04T20:53:08.472Z] RMSE (validation) = 0.9001440981626695 for the model trained with rank = 8, lambda = 0.2, and numIter = 10.
[2024-09-04T20:53:08.472Z] The best model was trained with rank = 8 and lambda = 0.2, and numIter = 10, and its RMSE on the test set is 0.9073522634082535.
[2024-09-04T20:53:08.472Z] The best model improves the baseline by 14.43%.
[2024-09-04T20:53:08.472Z] Movies recommended for you:
[2024-09-04T20:53:08.472Z] WARNING: This benchmark provides no result that can be validated.
[2024-09-04T20:53:08.472Z] There is no way to check that no silent failure occurred.
[2024-09-04T20:53:08.472Z] ====== movie-lens (apache-spark) [default], iteration 19 completed (10182.529 ms) ======
[2024-09-04T20:53:09.435Z] -----------------------------------
[2024-09-04T20:53:09.435Z] renaissance-movie-lens_0_PASSED
[2024-09-04T20:53:09.435Z] -----------------------------------
[2024-09-04T20:53:09.435Z]
[2024-09-04T20:53:09.435Z] TEST TEARDOWN:
[2024-09-04T20:53:09.435Z] Nothing to be done for teardown.
[2024-09-04T20:53:09.435Z] renaissance-movie-lens_0 Finish Time: Wed Sep 4 20:53:09 2024 Epoch Time (ms): 1725483189215